def t_sne(size_datapoints):
model.to('cpu')
model.eval()
with torch.no_grad():
dataloader = load.load_mnist(size_datapoints)
data = iter(dataloader)
images, labels = data.next()
mean, logvar = model.encoder(images)
z = model.reparameterize(
mean, logvar) # latent space representation of dataset
tsne = TSNE(n_components=2, random_state=0)
z_2d = tsne.fit_transform(
z) # Apply t-sne to convert to 2D (n_components) dimensions
target_ids = range(0, 10)
y = labels.detach().numpy(
) # need to detach labels from computation graph to use .numpy()
plt.figure(figsize=(6, 5))
colors = 'r', 'g', 'b', 'c', 'm', 'gold', 'k', 'gray', 'orange', 'chocolate'
for i, c in zip(target_ids, colors): # zip creates iterator
ind = np.where(y == i) # returns indices where y==i
plt.scatter(
z_2d[ind, 0], z_2d[ind, 1],
c=c) # plt.scatter(x-coordinate , y-coordinate , color)
plt.savefig(t_sne_save_directory + 't_sne_visualization.png')
plt.show()
plt.close()
model = model.to(device)
criterium = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=1e-4)
# Map
data = Dataset(src_data, tgt_data, 20 * 1000, batch_size_sents=1000)
data.create_order()
for epoch in range(4001):
# Definition of inputs as variables for the net.
# requires_grad is set False because we do not need to compute the
# derivative of the inputs.
samples = data.next()
batch = samples[0]
#batch.cuda()
# Set gradient to 0.
optimizer.zero_grad()
# Feed forward.
src = batch.get('source')
src = src.transpose(0, 1)
#print("Batch: " + str(src.size()))
src_mask = batch.get("src_mask")
src_length = batch.get("src_length")
targets = batch.get('target_input')
pred = model(src, src_mask, src_length)
def main():
global state
global classes
global writer
if not os.path.isdir(args.ckpt_dir):
os.makedirs(args.ckpt_dir)
training_dataset = datasets.CIFAR10(root='./data',
train=True,
download=True,
transform=transforms.Compose([
transforms.RandomCrop(32,
padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(
(0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
]))
training_dataloader = torch.utils.data.DataLoader(
training_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=2)
testing_dataset = datasets.CIFAR10(root='./data',
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
(0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
]))
testing_dataloader = torch.utils.data.DataLoader(
testing_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=2)
num_classes = len(classes)
model = vgg16(num_classes=num_classes, init_weights=False)
model = model.cuda()
data = iter(training_dataloader)
images, _ = data.next()
writer.add_graph(model, images.cuda())
writer.close()
crit = nn.CrossEntropyLoss()
opt = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
if args.start_from != 0:
ckpt_file = args.ckpt_dir + '/ckpt_epoch' + str(
args.start_from) + '.pth'
print('Loading from checkpoint {}'.format(ckpt_file))
assert (os.path.isfile(ckpt_file))
ckpt = torch.load(ckpt_file)
model.load_state_dict(ckpt["model_state_dict"])
opt.load_state_dict(ckpt["opt_state_dict"])
state = ckpt["state"]
for epoch in range(args.start_from, args.epochs):
adjust_lr(opt, epoch)
writer.add_scalar('Learning Rate', state["lr"], epoch)
writer.close()
print('Epoch: [%5d / %5d] LR: %f' %
(epoch + 1, args.epochs, state['lr']))
train(model, training_dataloader, crit, opt, epoch)
test_loss, test_acc = test(model, testing_dataloader, crit, epoch)
print("Test Loss: {:.5f} Test Acc: {:.2f}%".format(
test_loss, 100 * test_acc))
if epoch % 10 == 9:
save_checkpoint(
{
'epoch': epoch + 1,
'model_state_dict': model.state_dict(),
'acc': test_acc,
'opt_state_dict': opt.state_dict(),
'state': state
},
ckpt_dir=args.ckpt_dir)
if (G_step < 25 or G_step % 500 == 0):
Diters = 100
else:
Diters = 5
j = 0
###########################
# Train discriminator
###########################
while j < Diters and i < len(dataset):
netD.zero_grad()
netE.zero_grad()
netG.zero_grad()
# real data
j += 1
_data, _ = data.next()
_data = _data.view(-1, 28 * 28)
i += 1
real_data = torch.Tensor(_data)
if use_cuda:
real_data = real_data.cuda(gpu)
real_data_v = Variable(real_data)
# h input for G
h = netE(real_data_v.view(-1, 1, 28, 28))[ARGS.Glayer].data
if use_cuda:
h = h.cuda(gpu)
# train with real
D_real = netD(real_data_v)
D_real = D_real.mean()
else:
self.data, self.targets = img_test, target_test
def __getitem__(self, index):
img, target = self.data[index], int(self.targets[index])
img = img.reshape(34, 34, 10)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def __len__(self):
return len(self.data)
if __name__ == '__main__':
batch_size = 32
kwargs = {'num_workers': 1, 'pin_memory': True}
dataloader = torch.utils.data.DataLoader(NMNIST(
'../data', transform=transforms.Compose([transforms.ToTensor()])),
batch_size=batch_size,
shuffle=True)
data = iter(dataloader)
print(data.next()[0].shape)
# ==================Definition Start======================
def generate_image(x):
input.save(x.data, 'mnist', ARGS.samplepth, 'reals', 10)
# ==================Definition End======================
dataset = input.get_dataset('mnist', BATCH_SIZE)
sorted = torch.zeros(100, 1, 28, 28)
data = iter(dataset)
_data, _label = data.next()
sample = 0 # current sample
for i in range(10): # all digits
class_sample = 0 # track num of sample for each class
for j in range(len(_label)): # search through labels until we find a match
if (class_sample == 10):
break
elif (_label[j] == i):
sorted[sample, :] = _data[j]
sample += 1
digit_sample += 1
j += 1
# generate_image(Variable(sorted))
